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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

HiTEM

SAND2025-03438O HiTEM3d v1.0 is a user-friendly software designed to simulate how electromagnetic signals move through large geological environments, particularly around wellbores. It helps geophysicists and resource explorers understand subsurface conditions more effectively and affordably. By simplifying complex geological models, HiTEM3d allows users to visualize and analyze electromagnetic fields, making it easier to identify valuable resources like minerals or water. Its efficient processing capabilities ensure quick results, making it an essential tool for anyone involved in geophysical research or exploration. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Weiss, Chester [Sandia National Lab. (SNL-CA), Liv↗
DOE OSTI2025

A miniature CRISPR–Cas10 enzyme confers immunity by inhibitory signalling

Microbial and viral co-evolution has created immunity mechanisms involving oligonucleotide signalling that share mechanistic features with human antiviral systems1. In these pathways, including cyclic oligonucleotide-based antiphage signalling systems (CBASSs) and type III CRISPR systems in bacteria and cyclic GMP–AMP synthase–stimulator of interferon genes (cGAS–STING) in humans, oligonucleotide synthesis occurs upon detection of virus or foreign genetic material in the cell, triggering the antiviral response2, 3–4. Here, in an unexpected inversion of this process, we show that the CRISPR-related enzyme mCpol synthesizes cyclic oligonucleotides constitutively as part of an active mechanism that represses a toxic effector. Cell-based experiments demonstrated that the absence or loss of mCpol-produced cyclic oligonucleotides triggers cell death, preventing the spread of viruses that attempt immune evasion by depleting host cyclic nucleotides. Structural and mechanistic investigation revealed mCpol to be a di-adenylate cyclase whose product, c-di-AMP, prevents toxic oligomerization of the effector protein 2TMβ. Analysis of cells by fluorescence microscopy showed that lack of mCpol allows 2TMβ-mediated cell death due to inner membrane collapse. These findings unveil a powerful defence strategy against virus-mediated immune suppression, expanding our understanding of the role of oligonucleotides in immunity.

Doherty, Erin E↗
DOE OSTI2024

CNN-Based Phase Fault Classification in Real and Simulated Power Systems Data

This study proposes a convolutional neural network (CNN)–based two-step phase fault detection and identification method to classify anomalies in the power grid signal. Specifically, the first step checks the fault’s existence and determines the need for the second step. Subsequently, in the case of anomalies in the power grid signal, the second step identifies the type of fault, including line-to-line, single-line-to-ground, double-line-to-ground, and triple-line. Accordingly, the CNN architecture is both designed for the classification layers and trained with simulated data. To provide maximum prediction accuracy with minimum processing time, this study investigates the combinations of various feature extraction (FE) techniques, such as fast Fourier transform (FFT), amplitude and phase (AP), auto-correlation function, power spectral density, and wavelet transform (WT). Consequently, simulated and real-world results demonstrate that the proposed two-step method outperforms conventional one-step techniques, with the best performance obtained by using the combination of AP-AP, AP-WT, FFT-AP, and FFT-WT–based FE methods.

Alaca, Ozgur↗
DOE OSTI2024

Novel Signatures Beyond the Standard Model: From Extreme Environments to Precision Measurements

Most of our universe is dark. It is dark literally—visible matter makes up only 5% of the energy density—and conceptually, as many outstanding questions remain unsolved. The discovery of particle dark matter would shed light on our dark universe and throw open a window on the highest energy scales. This research made progress toward dark matter and new particle searches in three ways. Extreme compact objects, such as black holes, neutron stars, and stellar cores, can parametrically increase the production rates of feebly interacting particles and yield observable signatures. Specifically, a process known as superradiance leads to exponential growth of the number of ultralight particles gravitationally bound to rotating black holes, and turns astrophysical black holes into particle detectors. This research broadened the scope of applicability of superradiance studies to particles with a wide range of interactions and masses, predicting multi messenger signals. When ultralight particles are produced as dark matter in the early universe, they can be detected directly in laboratory experiments. However, these feebly interacting particles lead to small signals that are inevitably difficult to tease out from backgrounds; their detection requires the theoretical development of unique signatures that can be searched for in low-noise experiments. Some dark matter particles could be detectable with thin film metamaterials combined with low noise single-photon detectors. This research develop the theoretical framework and supported experimental efforts for ambitious searches for a wider class of ultralight particles. Overall, the outcome of this theoretical particle physics program developed novel signatures of new dark matter particles, and thereby expanded the particle parameter space which can be explored with astrophysical observations and precision experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC

We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure is evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the τ τ decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Improvement and generalization of ABCD method with Bayesian inference

To find New Physics or to refine our knowledge of the Standard Model at the LHC is an enterprise that involves many factors, such as the capabilities and the performance of the accelerator and detectors, the use and exploitation of the available information, the design of search strategies and observables, as well as the proposal of new models. We focus on the use of the information and pour our effort in re-thinking the usual data-driven ABCD method to improve it and to generalize it using Bayesian Machine Learning techniques and tools. We propose that a dataset consisting of a signal and many backgrounds is well described through a mixture model. Signal, backgrounds and their relative fractions in the sample can be well extracted by exploiting the prior knowledge and the dependence between the different observables at the event-by-event level with Bayesian tools. We show how, in contrast to the ABCD method, one can take advantage of understanding some properties of the different backgrounds and of having more than two independent observables to measure in each event. In addition, instead of regions defined through hard cuts, the Bayesian framework uses the information of continuous distribution to obtain soft-assignments of the events which are statistically more robust. To compare both methods we use a toy problem inspired by pp\to hh\to b\bar b b \bar b p p → h h → b b ‾ b b ‾ , selecting a reduced and simplified number of processes and analysing the flavor of the four jets and the invariant mass of the jet-pairs, modeled with simplified distributions. Taking advantage of all this information, and starting from a combination of biased and agnostic priors, leads us to a very good posterior once we use the Bayesian framework to exploit the data and the mutual information of the observables at the event-by-event level. We show how, in this simplified model, the Bayesian framework outperforms the ABCD method sensitivity in obtaining the signal fraction in scenarios with 1% and 0.5% true signal fractions in the dataset. We also show that the method is robust against the absence of signal. We discuss potential prospects for taking this Bayesian data-driven paradigm into more realistic scenarios.

Alvarez, Ezequiel↗
DOE OSTI2025

STILGAR End-of-Project Report

The Subsurface Tunnel Imaging LeveraGed by Analysis of Rayleigh wave ellipticity (STILGAR) project demonstrated an integrated geophysical approach for detecting, locating, and characterizing underground structural changes using dense seismic arrays and advanced inversion techniques. Field campaigns were conducted at two operational mines—the Redmond salt mine (Utah) and Graymont Pleasant Gap limestone mine (Pennsylvania)—providing real-world testbeds for monitoring anthropogenic subsurface activity. At the Redmond salt mine, seismic interferometry combined with back-projection inversion successfully identified continuous, low-amplitude signals from mining operations. The approach differentiated stationary from migrating anthropogenic sources, captured daily operational cycles, and validated the potential of passive seismic monitoring for remote detection of underground activity. At the Graymont Pleasant Gap mine, two dense seismic deployments in the spring and fall of 2023 generated over 4 TB of high-resolution data. Key outcomes included the relocation of 199 underground and 8 surface explosions with accuracies within tens of meters and the development of a 3D P-wave velocity model using the triple-difference tomography algorithm (tomoTD) that resolved major structural features such as the mine entrance, low-velocity tunnels, and roof-collapse areas. Ambient noise cross-correlation and back-projection analyses revealed persistent sources linked to ongoing mining activity, whereas horizontal-to-vertical spectral ratio (HVSR) and ellipticity studies confirmed stable site responses across seasons and identified soil thickness trends consistent with regional erosional and depositional processes. Checkerboard and sensitivity tests further validated the robustness of the tomographic results. Overall, the findings emphasize that although significant progress has been made in subsurface imaging, further work is needed to enhance the detection and localization of underground structures. Accurate imaging requires higher frequencies, yet anthropogenic sources tend to dominate the seismic record at those frequencies, and high-frequency surface waves are affected by higher modes that complicate interpretation. The improved detection and localization of human-induced signals enabled detailed temporal and spatial mapping of daily mine operations, demonstrating the feasibility of continuous anthropogenic source monitoring. Sensitivity to signals from nontraditional sources, such as fan operations, highlights the broader applicability of this approach to other industrial environments in which continuous and impulsive signals are present. The field campaigns produced a substantial volume of high-quality seismic data, supporting the development and testing of new methods for seismic source characterization and subsurface imaging. Future deployments should include sensors capable of recording lower frequencies to probe deeper structures, increase bandwidth to enhance resolution and sensitivity to both shallow and deep targets, and collect additional large-scale datasets to refine imaging and source characterization techniques. Moreover, conducting 3D modeling studies of seismic wavefields at higher frequencies will provide a better understanding of wave scattering and cavity–wavefield interactions in complex underground environments. In conclusion, the STILGAR project demonstrated that integrated seismic monitoring can effectively characterize underground operations, capturing both natural and anthropogenic signals. The approaches developed provide a foundation for improved detection, localization, and imaging of subsurface structures and are directly transferable to broader industrial monitoring applications.

58 GEOSCIENCES↗
DOE OSTI2026

RTN-117: Image Calibration and Instrument Signal Removal for the First Year of the LSST

The NSF-DOE Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) requires calibration products that provide uniform, stable, and accurate photometric and astrometric performance across the 3.2 gigapixel focal plane and throughout the 10-year survey. This paper details the algorithms and workflows used to produce instrument calibrations and remove instrumental artifacts for Data Preview 2 (DP2)---the first end-to-end processing demonstration using on-sky data with the LSST Camera (LSSTCam). We describe the verification, acceptance, and certification framework used to assess calibration quality and quantify residual systematics. We show baseline metrics on calibrated science images to evaluate the robustness of the calibration and instrument signature removal (ISR) data processing pipelines for DP2. Finally, we summarize the known limitations observed in DP2 production and outline expected algorithmic improvements for the first public LSST data release (Data Release~1, DR1).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Impact of neutrino flavor conversions on neutron star merger dynamics, ejecta, nucleosynthesis, and multimessenger signals

We present numerical relativity simulations of binary neutron star mergers incorporating neutrino flavor transformations triggered by fast flavor instability, quantum many-body effects, or potential beyond standard model physics. In both long-lived and short-lived remnant scenarios, neutrino flavor conversions modify species-dependent neutrino luminosities and mean energies, and drive the matter towards more neutron rich conditions. They produce up to more neutron rich ejecta and significantly boost the r-process yields, especially in low-density, near-equatorial outflows. We identify regions unstable to fast flavor instabilities and find that these instabilities persist despite flavor conversions. We further test the sensitivity to the equilibration timescale of the flavor conversions, finding that slower flavor conversions can interact with thermodynamic equilibration, and increase the neutron richness of the ejecta. Flavor conversions may also contribute to stronger gravitational wave and neutrino emissions, pointing to a correlation between neutrino transport and merger dynamics. Furthermore, these results highlight the potential impact of flavor conversions while motivating future work to improve on theoretical understanding of flavor instabilities in global simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Elucidating the Interfacial Barriers in Lanthanide Back-Extraction: From Water to Oil and Back Again

Recovery of critical rare earth elements from complex mixtures has long been realized via solvent extraction, where ions in an aqueous phase are separated into an organic phase using amphiphilic ligands. While a great deal of effort has been placed on understanding this forward reaction, substantial knowledge gaps in the back-extraction process remain. This includes the mechanism of interfacial dissociation and transport back into a highly acidic aqueous phase for further processing. In this work, we connect back-extraction kinetics made in realistic solvent extraction systems to salient interfacial chemistry and structure that represent bottlenecks in the back-extraction of lanthanide ions. We show that the interface between the two liquid phases varies dramatically based on the composition of both phases. Water stretching signals are shown to report on the population of lingering interfacial complexes and are thus used as a reporter of competitive adsorption from excess free ligands in solution for limited interfacial vacancies. We show that excess free ligands, often used to improve forward extractions, set up interfacial blockades inhibiting back-extraction both kinetically and thermodynamically. In conclusion, this insight opens up avenues to tune interfacial properties to facilitate a more dynamic, exchangeable interface to speed up back-extractions while using less energy intensive chemical swings.

Interfaces↗
DOE OSTI2026

Importance of Spatially Continuous Urban Surface Properties in Urban‐Resolving Earth System Modeling

Accurate representation of urban properties and processes at higher resolutions in global modeling systems is essential for advancing our ability to capture the complexities of urban systems and informing effective resilience strategies. However, the prescription of coarse global-scale urban properties in most state-of-the-art Earth system models (ESMs) is limiting their potential for capturing urban signals as they advance toward kilometer-scale simulation capabilities. To bridge this gap in inadequate urban property representation and to advance urban-resolving Earth system modeling, this work integrates the newly-developed global 1 km-resolution facet-level urban surface property data set, U-Surf, into the land component of Community Earth System Model (CESM)—Community Terrestrial System Model (CTSM). The land-only CTSM simulations are validated against satellite measurements, ground-based urban weather stations, flux tower observations, and reanalysis data. Results demonstrate that the enhanced urban properties allow improved simulations of urban meteorology and surface energy fluxes compared to the default coarse-resolution categorical urban canopy parameters. Spatial scaling analysis reveals regime-dependent information loss during resolution aggregation, as well as substantial scale-dependent variations in urban surface energy flux representation. Furthermore, these findings have critical implications for coupled Earth system modeling when including the effect of land-atmosphere interaction. This work establishes a foundation for future urban-resolving kilometer-scale ESM development, which will enable systematic intra- and inter-city comparisons that inform urban adaptation strategies across diverse global urban environments.

Cheng, Yifan [University of Illinois Urbana-Champa↗
DOE OSTI2026

Development of a pattern recognition algorithm for reconstructing multi-particle events in the Mu2e detector

Mu2e is an upcoming experiment at Fermilab and its main goal is to search for the Charged Lepton Flavor Violation (CLFV) in the coherent transition of a muon into an electron on an Al target. In Mu2e, multi-particle events can occur simultaneously within the same time region and it is crucial to accurately identify each particle track, including signals, to improve the robustness of track finding methods and enhance reconstruction efficiency. ¯p annihilation is one of the background events and produces multiple particles that can mimic signal events. Additionally, photons from radiative pion captures can produce a γ → $e+e−$ pair, which can be used to calibrate the Mu2e momentum scale and the resolution. The Mu2e track reconstruction sequence begins by grouping hits produced in the tracker based on time and z coordinate information, called TimeCluster, and selected hits are processed to reconstruct helices and determine their momentum. The current pattern recognition algorithms identify a single helix per TimeCluster for single track events. A new pattern recognition algorithm is being developed to reconstruct multi-particle events and its features for finding multiple tracks and the current evaluation results are reported.

Kitagawa, H. [Pisa U.]↗
DOE OSTI2024

Neutron-antineutron oscillation sensitivity study at DUNE

The Deep Underground Neutrino Experiment (DUNE) aims to measure neutrino oscillations as well as search for beyond the standard model physics such as baryon number violating (BNV) processes. DUNE will use a 70 kt Liquid Argon Time Projection Chamber (LArTPC) located more than 1 km underground. A promising BNV process is neutron-antineutron oscillation ($n \rightarrow \bar{n}$) which, if discovered, would offer unique insight into the baryon asymmetry of the universe. We are developing a classification algorithm that separates $n \rightarrow \bar{n}$ events from major background atmospheric neutrino interactions using DUNE far detector simulations. We will perform the classification of signals and backgrounds by analyzing key features such as the multiplicity, isotropy, and kinematics of the reconstructed events. In the future, this algorithm can be used to obtain the sensitivity of the DUNE detectors to the neutron-antineutron oscillation lifetime.

Wheeler, Justin↗
DOE OSTI2025

Injection locking and coupling dynamics in superconducting nanowire-based cryogenic oscillators

Oscillators designed to function at cryogenic temperatures play a critical role in superconducting electronics and quantum computing by providing stable, low-noise signals with minimal energy loss. Here, in this work, we present a comprehensive numerical study of injection locking and mutual coupling dynamics in superconducting nanowire (ScNW)-based cryogenic oscillators. Using the design space of a standalone ScNW-based oscillator, we investigate two critical mechanisms that govern frequency synchronization and signal coordination in cryogenic computing architectures: (1) injection locking induced by an external AC signal with a frequency near the oscillator's natural frequency, and (2) the mutual coupling dynamics between two ScNW oscillators under varying coupling strengths. We identify key design parameters—such as shunt resistance, nanowire inductance, and coupling strength—that govern the locking range. Additionally, we examine how the amplitude of the injected signal affects the amplitude of the locked oscillation, offering valuable insights for power-aware oscillator synchronization. Furthermore, we analyze mutual synchronization between coupled ScNW oscillators using capacitive and resistive coupling elements. Our results reveal that the phase difference between oscillators can be controlled by tuning the coupling strength, enabling programmable phase-encoded information processing. These findings could enable building ScNW-based oscillatory neural networks, synchronized cryogenic logic blocks, and on-chip cryogenic resonator arrays.

Artificial neural networks↗
DOE OSTI2026

Characterization of the First Prototype of the L1K65n Differential-Output Charge-Sensitive Preamplifier ASIC for High-Performance, Low-Background HPGe Detector Readout

Next-generation neutrinoless double-beta (0νββ) decay searches require a large detector/isotope mass and very low radioactive backgrounds. The upcoming LEGEND-1000 experiment (Large Enriched Germanium Experiment for Neutrinoless double-beta Decay) constitute a ton-scale array of several hundred high-purity germanium (HPGe) detectors enriched in 76Ge, the isotope of interest. It aims to reach a sensitivity beyond 10 28 years on the 0νββ decay half-life of 76Ge, necessitating an order of magnitude reduction in background with respect to the state of the art. This will require the implementation of a dedicated application specific integrated circuit (ASIC) preamplifier that will significantly lower background contribution compared to the conventional low-mass front end (LMFE) employed in the Majorana Demonstrator and LEGEND-200 experiments while maintaining or improving energy resolution and other performance parameters. Features include a high (9 MeV) dynamic range with noise low enough to achieve a trigger threshold of <1 keV, a single power supply to reduce background-inducing cabling, and a differential output to maintain signal integrity over several meters of transmission line. The chip would be optimized to operate in liquid argon (87 K) and be able to do so reliably for ten years. Following on from an earlier 180-nm prototype, we have developed a differential-output charge-sensitive preamplifier ASIC fabricated in a 65-nm process, 1×2 mm in size, that addresses these needs. Here, we present recent results from the successful testing and characterization of the first version of this device.

ASIC↗
DOE OSTI2026

Scalable multiplexed machine learning gas sensor chips for food classification

Multiplexed gas sensor arrays combined with machine learning have unlocked previously inaccessible applications for scent-based sensing. Current platforms are limited by overlapping sensing materials with similar compositions, leading to highly correlated responses, or multistep deposition processes that hinder scalability. In this work, we developed a 16-element monolithic chip with fully distinct sensing layers, enabling a truly heterogeneous array. The system consists of highly sensitive carbon nanotube field effect transistors that are functionalized through a single-step microdispensing method compatible with automated pipetting systems. The resulting chip produces characteristic signal patterns in response to object-specific scent profiles and, when combined with machine learning algorithms, can perform automated object identification. We demonstrate the classification of 16 different objects, including food spoilage and nut allergens, with a 92.6% overall prediction accuracy.

Bassil, Carla [University of California, Berkeley,↗
DOE OSTI2026

Strong-field Driven Sub-cycle Band Structure Modulation and Dephasing Control

In this work, we present measurements of ultrafast electric-field observables in magnesium oxide using a non-resonant nonlinear optical interaction. Using field observables, we show that strong laser fields modulate the band structure on sub-cycle timescales, thereby altering the material’s nonlinear optical response. We perform time-dependent perturbation theory calculations using a field-dependent dispersion relation and semiconductor Bloch equation calculations, both of which agree with experimental observations. Furthermore, we extract pulse decay times from the real-time signal electric field envelope and show sub-cycle control of dephasing times. Our work offers a new perspective on strong-field-driven electron dynamics in solids through electric-field observables. The demonstrated attosecond modulation of the nonlinear response could have important implications for quantum light generation and quantum spectroscopy using nonlinear optical processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

PSU-BSEC Doppler Lidar Processed Scans

This data repository includes the processed vertically staring measurements (stare files) and the angled scans (profile files) necessary to calculate horizontal winds retrieved by the Pennsylvania State University (PSU) Doppler Lidar as a part of the Baltimore Social-Environmental Collaborative Urban Integrated Field Laboratory (BSEC UIFL). The PSU Doppler Lidar measures aerosol backscatter intensity (m-1 sr-1), signal-to-noise ratio, and radial velocity (i.e., vertical velocity in the case of stare files, units: m/s) at approximately 1 Hz temporal resolution and 30 m spatial resolution. Within the "stare_data" subdirectory there exist example figures of all retrieved data. For further information, please email Nicholas Prince, nec5299@psu.edu.

Air Quality↗